---
title: "App sample"
author: "Jorge Velez"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

Visualizations 1 {data-icon="fa-signal"}
===================================== 

Page
    
```{r, include=FALSE}
library(leaflet)
library(stringr)
library(tibble)
library(tidyverse)
library(dplyr)
library(plyr)
library(leaflet.extras)
library(scales)
library(sp)

# Load library
library('sf')


library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)

# Make some noisily increasing data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
# Load shapefile
col <- read_sf('C:/Users/velez/Desktop/Shiny R/leaflet/mychapter/Tasa_de_repitencia_en_el_sector_oficial_desde_transicion_hasta_grado_once.shp')
summary(col)

# Print the slot names of `shp`
slotNames(col)

# Glimpse the data slot of shp
glimpse(col)

# Print the class of `shp`
class(col)

# map the polygons in shp
col  %>% 
  leaflet() %>% 
  addTiles() %>% 
  addPolygons()

# summarize the mean income variable
summary(col$BasicaSecu)
####Map
nc_pal <- colorNumeric("YlGn", domain = NULL)
col_map <-
  leaflet(col) %>%
  addTiles() %>%
  addPolygons(weight = 1, fillOpacity = 1,
              color = ~nc_pal(BasicaSecu),
              label = ~paste0(Departamen, ": ", BasicaSecu),
              highlight = highlightOptions(weight = 3,
                                           color = "red",
                                           bringToFront = TRUE)) %>%
  addLegend(pal = nc_pal, values = ~BasicaSecu, opacity = 0.7, title = "Repeat rate in secondary Education",
            position = "topright") %>%
  setView(lat = 4.624335, lng = -74.063644, zoom = 4)

```
### Map 1
-----------------------------------------------------------------------


### Geometry smooth with Loess Smoothed Fit
```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p)
```
```

### Colombian map: Children's Repeat rate (Secondary)
```{r, echo=FALSE, fig.dim = c(6, 4)}
col_map
```

Visualisations 2
=======================================================================

Row
-----------------------------------------------------------------------

### stat_density Example

```{r}
dfGamma = data.frame(nu75 = rgamma(100, 0.75),
           nu1 = rgamma(100, 1),
           nu2 = rgamma(100, 2))

dfGamma = stack(dfGamma)

p <- ggplot(dfGamma, aes(x = values)) +
            stat_density(aes(group = ind, color = ind),position="identity",geom="line")
ggplotly(p)
```

### A scatter 
```{r}
library(plotly)
library(gapminder)
p <- gapminder %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp, 
    size = ~pop, 
    color = ~continent, 
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers'
  ) %>%
  layout(
    xaxis = list(
      type = "log"
    )
  )
p
```

Row
-----------------------------------------------------------------------
### A geom. density
```{r}
dd<-data.frame(matrix(rnorm(144, mean=2, sd=2),72,2),c(rep("A",24),rep("B",24),rep("C",24)))
colnames(dd) <- c("x_value", "Predicted_value",  "State_CD")

dd <- data.frame(
  predicted = rnorm(72, mean = 2, sd = 2),
  state = rep(c("A", "B", "C"), each = 24)
)

grid <- with(dd, seq(min(predicted), max(predicted), length = 100))
normaldens <- ddply(dd, "state", function(df) {
  data.frame(
    predicted = grid,
    density = dnorm(grid, mean(df$predicted), sd(df$predicted))
  )
})

p <- ggplot(dd, aes(predicted))  +
            geom_density() +
            geom_line(aes(y = density), data = normaldens, colour = "red") +
            facet_wrap(~ state)
ggplotly(p)
```

### A scatterplo sample
```{r}
# Source: https://github.com/dgrtwo/gganimate
# install.packages("cowplot")  # a gganimate dependency
# devtools::install_github("dgrtwo/gganimate")
library(ggplot2)
library(gganimate)
library(gapminder)
theme_set(theme_bw())  # pre-set the bw theme.

p <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent, frame = year)) + 
  geom_point() + 
  transition_time(year) +
  ggtitle("Year: {frame_time}") +
  transition_time(year) +
  ease_aes("linear") +
  enter_fade() +
  exit_fade() 
# animate
animate(p)
```


Tables {data-icon="fa-table"}
=====================================     

### Table 1
    
```{r}
```
    
### Table 2

```{r}
```